Secrets of successful data analysis - Sykalo Eugene 2023
Descriptive Statistics - Overview of descriptive statistics and their applications
Data Analysis Tools and Techniques
Introduction to Descriptive Statistics
Descriptive statistics is a branch of statistics that deals with the collection, analysis, interpretation, and presentation of data. It is concerned with summarizing and describing the main features of a dataset, such as the mean, median, mode, range, variance, and standard deviation. The purpose of descriptive statistics is to provide a clear and concise summary of the data, which can be used to make informed decisions and draw meaningful conclusions.
In order to understand descriptive statistics, it is important to first understand the different types of data and their characteristics. There are two main types of data: quantitative and qualitative. Quantitative data can be measured and expressed numerically, while qualitative data is non-numerical and can be categorized based on its properties or characteristics.
Descriptive statistics is used to summarize and describe quantitative data. The most common measures of central tendency used in descriptive statistics are the mean, median, and mode. The mean is the arithmetic average of all the values in a dataset, while the median is the middle value in a dataset when it is ordered from smallest to largest. The mode is the value that appears most frequently in a dataset.
| Measure of Central Tendency | Description | Formula |
| Mean | The arithmetic average of all the values in a dataset. | Mean = Σx / n |
| Median | The middle value in a dataset when it is ordered from smallest to largest. | If n is odd: Median = x[(n+1)/2] If n is even: Median = (x[n/2] + x[(n/2)+1])/2 |
| Mode | The value that appears most frequently in a dataset. | Mode = value with highest frequency |
Measures of variability are used to describe the spread or dispersion of the data. The range is the difference between the largest and smallest values in a dataset, while the variance and standard deviation measure the amount of variation from the mean. Variance is the average of the squared differences from the mean, while standard deviation is the square root of the variance.
| Measure of Variability | Description | Formula |
| Range | The difference between the largest and smallest values in a dataset. | Range = max(x) - min(x) |
| Variance | The average of the squared differences from the mean. | Variance = Σ(x - μ)² / (n - 1) |
| Standard deviation | The square root of the variance. | Standard deviation = √(Σ(x - μ)² / (n - 1)) |
Descriptive statistics also includes measures of distribution, such as skewness and kurtosis, which describe the shape of a distribution. Skewness measures the asymmetry of the distribution, while kurtosis measures the peakedness or flatness of the distribution.
| Measure of Distribution | Description | Formula |
| Skewness | Measures the asymmetry of the distribution. A skewness coefficient of zero indicates that the distribution is symmetric, while a positive skewness coefficient indicates that the distribution is positively skewed and a negative skewness coefficient indicates that the distribution is negatively skewed. | Skewness coefficient = (Σ(x - μ)³ / n) / (σ³) |
| Kurtosis | Measures the peakedness or flatness of the distribution. A kurtosis coefficient of three indicates that the distribution is normal, while a kurtosis coefficient greater than three indicates that the distribution is leptokurtic and a kurtosis coefficient less than three indicates that the distribution is platykurtic. | Kurtosis coefficient = (Σ(x - μ)⁴ / n) / (σ⁴) - 3 |
Measures of Central Tendency
Measures of central tendency are used in descriptive statistics to describe the typical or central value of a dataset. The three most common measures of central tendency are the mean, median, and mode.
The mean is the arithmetic average of all the values in a dataset. It is calculated by adding up all the values in the dataset and dividing by the number of values. The mean is sensitive to extreme values, or outliers, in the dataset, which can skew the result. In such cases, the median may be a better measure of central tendency.
The median is the middle value in a dataset when it is ordered from smallest to largest. It is less sensitive to outliers than the mean and is often used when a dataset is skewed or has extreme values. The median is a good measure of central tendency when the data is not normally distributed.
The mode is the value that appears most frequently in a dataset. It is useful when the dataset has a high frequency of repeated values. Unlike the mean and median, the mode can be used for both numerical and categorical data.
Choosing the appropriate measure of central tendency depends on the type of data and the purpose of the analysis. The mean is the most commonly used measure of central tendency, but it is important to consider the distribution of the data and the presence of outliers before choosing a measure.
Measures of Variability
Measures of variability are used in descriptive statistics to describe the spread or dispersion of the data. They provide information about how much the data varies from the central tendency measures such as mean, median, and mode. The three most common measures of variability are range, variance, and standard deviation.
Range is the simplest measure of variability and is calculated as the difference between the largest and smallest values in a dataset. It provides information about the spread of the data but is sensitive to outliers. Range is easy to calculate, but it does not take into account the distribution of the data.
Variance and standard deviation are more sophisticated measures of variability that take into account the distribution of the data. Variance is a measure of the average squared deviations from the mean, and is calculated by subtracting the mean from each data point, squaring the result, and then adding up all the squared values and dividing by the number of data points minus one. Variance is expressed in units squared, which can be difficult to interpret.
Standard deviation is the square root of the variance and is expressed in the same units as the original data. It provides a measure of the average distance of the data points from the mean. Standard deviation is used more commonly than variance because it is in the same units as the original data and is easier to interpret.
A low standard deviation indicates that the data is clustered around the mean, while a high standard deviation indicates that the data is more spread out. Standard deviation is often used in combination with the mean to describe a dataset. For example, if the mean is 10 and the standard deviation is 2, we can say that most of the data falls within the range of 8 to 12.
Measures of Distribution
Measures of distribution are used to describe the shape of a distribution. The two most common measures of distribution are skewness and kurtosis.
Skewness measures the asymmetry of the distribution. A distribution is symmetric if it is evenly distributed around the mean, with half of the data points above the mean and half below. A distribution is positively skewed if it has a long tail to the right, meaning that there are more data points with high values than with low values. A distribution is negatively skewed if it has a long tail to the left, meaning that there are more data points with low values than with high values.
Skewness is measured using the skewness coefficient, which is calculated as the third standardized moment of the distribution. A skewness coefficient of zero indicates that the distribution is symmetric, while a positive skewness coefficient indicates that the distribution is positively skewed and a negative skewness coefficient indicates that the distribution is negatively skewed.
Kurtosis measures the peakedness or flatness of the distribution. A distribution is said to be leptokurtic if it has a high peak and long tails, meaning that there are more data points clustered around the mean than expected under a normal distribution. A normal distribution has a kurtosis of three. A distribution is said to be platykurtic if it has a low peak and short tails, meaning that there are fewer data points clustered around the mean than expected under a normal distribution.
Kurtosis is measured using the kurtosis coefficient, which is calculated as the fourth standardized moment of the distribution. A kurtosis coefficient of three indicates that the distribution is normal, while a kurtosis coefficient greater than three indicates that the distribution is leptokurtic and a kurtosis coefficient less than three indicates that the distribution is platykurtic.
Understanding the shape of a distribution is important for data analysis because it provides information about the underlying data generating process. For example, if a distribution is positively skewed, it may indicate that there are outliers or that the data is non-normal. Similarly, if a distribution is leptokurtic, it may indicate that there are more extreme values than expected under a normal distribution, which may have implications for statistical modeling and inference.
Applications of Descriptive Statistics
Descriptive statistics has many applications in research and business. One of the primary applications is in data visualization techniques. Descriptive statistics can be used to create charts, graphs, and other visual representations of data that make it easier to understand and interpret. Some common data visualization techniques include histograms, bar charts, scatter plots, and box plots.
Another application of descriptive statistics is in reporting and presenting data. Descriptive statistics can be used to summarize and present data in a clear and concise manner. For example, a report might include a table or chart that summarizes the mean, median, and standard deviation of a dataset.
Descriptive statistics is also used in practical examples of research and business. For example, in market research, descriptive statistics can be used to analyze customer survey responses and identify patterns and trends in the data. In finance, descriptive statistics can be used to analyze stock prices and other financial data to make investment decisions.